The ability to sense and identify matter using automated equipment has been known for many years and is important to the general field of testing that has application in the physical sciences and across a broad spectrum of modern pursuits that rely on the physical sciences, including manufacturing, medicine, government regulation, e.g., regulation of pollutants, air quality, etc., detection of harmful substances, e.g., substances such as anthrax, nerve agents and other agents used in biological and chemical weapons, and analytes that are outgassed or otherwise given off by harmful substances, e.g., explosives such as trinitrotoluene (TNT) and cyclotrimethylene trinitramine (a.k.a. RDX, cyclonite or hexogen), among many others. Conventional sensing and identifying methods that have been used to detect one or more of the analytes mentioned above and/or other analytes include ion mobility spectrometry, flame photometry, mass spectrometry, electrochemistry, detection paper methods, surface acoustic wave methods, laser-induced breakdown spectroscopy, photo ionization detection, gas chromatography and cavity-ring-down spectroscopy.
The detecting methods just mentioned are generally equipment-centric, i.e., a sample believed to contain the analyte under consideration must be captured and placed either within, or at least in close proximity to, the equipment that either performs or is used in performing the corresponding method. However, equipment-centric methods are generally not suited to a number of applications, especially applications where it is, among other things: (1) difficult or impractical to place testing equipment at the location of the analyte to be tested; (2) difficult or impractical to retrieve a sample of the analyte from a particular location and test the sample at a location away from the location where the sample was obtained and/or (3) dangerous to place testing equipment at, and retrieve a sample from, the location where the analyte may be present. In these applications it is desirable to utilize a remote sensing and identifying method.
Important attributes of the equipment, i.e., “sensor,” used to perform a detecting method, either equipment-centric or remote, include selectivity, sensitivity and response time. An additional attribute that can be important for a remote-detecting sensor is range. Generally, “selectivity” refers to the ability of a sensor to discriminate between an analyte of interest and one or more interferents. For example, organo-phosphate insecticides, such as malathion, parathion, etc., are common interferents in detecting certain toxic nerve agents, e.g., GA (Tabun), GB (Sarin), GD (Soman), GF, VX, etc. As another example, Bacillus subtilis is a common interferent in detecting Bacillus anthracis (anthrax). “Sensitivity” generally refers to the ability of the sensor to detect low concentrations levels of the analyte of interest and is often measured in particles per liter (volume concentration) or particles per square meter (surface concentration). In the case of microorganisms, the appropriate concentration units may be colony forming units (CFUs) per liter (volume concentration) or CFUs per square meter (area concentration). Response time generally refers to the elapsed time it takes the sensor to detect and identify the analyte of interest as measured from the time the sensor is either triggered (in the case where the sensor is triggerable) or the analyte first becomes available for detection (in the case where the sensor is continuously seeking to detect a particular analyte). For many conventional sensors, response time increases with decreasing concentrations. Range generally refers to the maximum physical distance between the sensor and the analyte at which a particular concentration of the analyte can be detected. For sensors that are used to quantify the amount of analyte present, the dynamic range is also an important capability. Dynamic range refers to the minimum and maximum amount of analyte that can be quantified.
Examples of conventional remote detectors include Raman spectroscopy, photoluminescence, Fourier transform infrared (FTIR) detectors, forward looking infrared (FLIR) detectors and differential absorption light detection and ranging (LiDAR) (DIAL) detectors. However, conventional embodiments of these detectors have one or more drawbacks or undesirable limitations under certain circumstances.
For example, Raman based sensors illuminate samples with ultraviolet light and look for a Raman shift in the reflected signal. Unfortunately, the atmosphere strongly absorbs infrared light severely limiting the range and sensitivity of such systems. Furthermore, the Raman shift is a very inefficient process and, therefore, has a severely limited sensitivity. Photoluminescence illuminates a sample with ultraviolet light and looks for re-radiated IR photons. Only a limited number of chemical compounds such as aromatic hydrocarbons will photoluminesce. Therefore this approach is limited in the type of analytes it can detect. In addition, it suffers from drawbacks in sensitivity and range because the ultraviolet light required is absorbed strongly by the atmosphere. Furthermore, photoluminescence is not very selective.
FTIR sensors suffer from several operational drawbacks when attempting to use such devices as remote sensors. First, FTIR sensors rely on an interferometer that generally requires the instrument to be stationary while acquiring measurement data. Second, it is necessary to record a background reference that is free of the analyte of interest prior to detecting that analyte. This limits the operational flexibility and mobility of FTIR sensors. For example, when moving to a new location for detecting analyte in a new region, it is essential to use other detectors to ensure that the analyte of interest is not present in the new region before recording background spectra. Once a background reference has been obtained, the FTIR sensor will then detect if the analyte of interest enters the new region. Moving an FTIR sensor to yet another location requires that the steps for obtaining a proper background reference be repeated. Therefore, FTIR sensors are not suitable for detection of analytes on the move. In addition to these flexibility and mobility issues, the infrared light sources used in FTIR detectors typically lack spectral intensity, thereby limiting the sensitivity, and range of the sensors.
A FLIR sensor uses a FLIR detector array and a set of filters that allows a user to visually detect the presence of certain chemical analytes. The sensitivity and selectivity of FLIR detection are highly dependent on the user's ability to interpret contrasts created in the visual field by looking at a scene using various different filters. FLIR detection is generally limited to sensing and identifying simple analytes, such as certain chemicals, and is unsuitable for identifying microorganisms, such as bacteria. Furthermore, this form of sensor is not easily automated and therefore requires a trained and vigilant person to perform detection.
Many DIAL sensors use carbon dioxide lasers to identify chemical analytes. One drawback of carbon dioxide lasers is that they are limited to using the spectral lines available from the carbon dioxide gain media. This limited wavelength selection limits the sensitivity and selectivity of prior art DIAL sensors. For example, FIG. 1A shows spectral absorption curves of the chemical warfare agents DMMP, GA, GB, GD, DPMP and TEP, along with the laser lines L that can be produced by a carbon dioxide laser. Note that the best line L1 available from a carbon dioxide laser for detecting agents GB and GD is at only half of the absorption peak of agent GB, limiting sensitivity to half of what would otherwise be achievable. In addition, there is no carbon dioxide laser line L available at the primary absorption peak PGA of agent GA. Furthermore, carbon dioxide laser based DIAL sensors lack the ability to generate and detect the broad spectral information required to identify micro-organisms, such as bacteria. Furthermore, these carbon dioxide laser systems are large, heavy, and require a large amount of power to operate.
In addition to the previous limitations, prior art carbon dioxide laser DIAL systems are limited in the pulses per second they can produce. Typical systems produce one set of multi-wavelength pulses per second. Since the signal-to-noise (S/N) ratio of a system can be improved by co-adding multiple measurements, the number of measurements that can be made per second is an important determinant in the response time/sensitivity trade-off of a sensor. The S/N ratio of a system improves with the square root of the number of co-added measurements. Therefore, if two systems have equal S/N ratios per measurement and system A performs one measurement per second and system B performs a million measurements per second, then system B can improve its sensitivity by a factor of 1,000 over system A without any increase in response time. Alternatively, System B can achieve the same sensitivity and reduce system response time by a factor of 1 million.
More recently, a DIAL sensor was developed that uses a quantum cascade (QC) laser to provide spectral information. Nelson, Shorter, Micmanus and Zahniser report using a QC-laser-based DIAL sensor to perform sub-part-per-billion detection of trace gases in their paper, “Sub-part-per-billion detection of nitric oxide in air using a thermoelectrically cooled mid-infrared quantum cascade laser spectrometer,” Applied Physics B Vol. 75, 2002, pp. 345-50, which is incorporated herein by reference in its entirety. In the Nelson et al. approach, the optical output frequency of the QC laser is swept with a bias ramp applied through a bias tee in a pulsed manner. The output of the QC laser is passed into a multi-pass gas cell that contains a sample either suspected or known to contain a particular chemical analyte. A broadband infrared detector is used to detect the output of the gas cell.
The Nelson et al. sensor suffers from several drawbacks and limitations. First, the spectral resolution of the sensor is limited by the relatively wide spectral pulses of the QC laser, thereby causing reduced selectivity. Second, these spectrally wide pulses can result in reduced sensitivity if the laser line-width is wider than the spectral absorption feature to be detected. Third, in order to maintain as narrow a spectral pulse width as possible (and, thus, maximizing spectral resolution) the QC laser is operated at low power, i.e., near its operating threshold, thereby limiting both range and sensitivity. Fourth, the sensor is prone to saturation because the laser is operated at low power. In other words, if the sensor is used to quantify the amount of analyte present it is limited in the maximum concentration it can measure by the power of the laser pulse used. Fifth, the Nelson et al. method collects the spectrum of the sampled gas sequentially over a series of laser pulses, thereby increasing detection, and response, time.
FIG. 1B shows a plot 10 of a series of three pulses 12A, 12B, 12C of the pulsed laser output power as a function of wavelength at three different times, t1, t2, t3, as used in the Nelson et al. method. Again, it is noted that the output power of the QC laser is kept close to the operating threshold of the laser so as to minimize the spectral width and maximize the spectral resolution. The output wavelength of the QC laser is shifted by applying a voltage ramp through a bias tee. FIG. 1C is an exemplary plot 16 of a spectral absorption profile 18 of the analyte that is desired to be detected using the laser output shown in FIG. 1B. Note the presence of a “valley” feature 22 of profile 18 that forms two “peak” features 24A, 24B. FIG. 1D is a plot 28 of the pulsed laser output of FIG. 1B as detected over a period that includes times t1, t2, t3 after passing through the gas with absorption characteristics shown in FIG. 1C by the Nelson et al. broadband infrared sensor. FIG. 1D clearly shows that there is significant “blurring” of features 22, 24A, 24B of plot of FIG. 1C that has completely masked these features. Generally, this is so because of the scanning of the absorption band with pulses of FIG. 1B that have widths W that are wider than any of individual features of absorption profile 18 of FIG. 1C. Mathematically, the broadband nature of pulses 12A-C is a power integrator, with the detected power of FIG. 1C being a convolution of the pulses of FIG. 1B with the absorption profile 18 of FIG. 1C. This example illustrates the drawbacks in sensitivity, selectivity, and dynamic range of Nelson et al.